Examples

Load Dataset

• load data as graphs in pytorch_geometric format:

  from flowbench.dataset import FlowDataset
  dataset = FlowDataset(root="./", name="montage")
  data = dataset[0]
  

  The data contains the structural information by accessing data.edge_index, and node feature information data.x.

• load data as tabular data in pytorch format:

  from flowbench.dataset import FlowDataset
  dataset = FlowDataset(root="./", name="montage")
  data = dataset[0]
  Xs = data.x
  ys = data.y
  

  Unlike the graph pyg.data, the data only contains the node features.

• load data as tabular data in numpy format:

  from flowbench.dataset import FlowDataset
  dataset = FlowDataset(root="./", name="montage")
  data = dataset[0]
  Xs = data.x.numpy()
  ys = data.y.numpy()
  

  This is the same as the previous one, but the data is in numpy format, which is typically used in the models from sklearn and xgboost.

• load text data with huggingface interface. We have uploaded our parsed text data in the huggingface dataset. You can load the data with the following code:

  from datasets import load_dataset
  dataset = load_dataset("cshjin/poseidon", "1000genome")
  

  The dataset is in the format of dict with keys train, test, and validation.

PyOD Models

Type	Abbr	Algorithm	Year	Class
Probabilistic	ABOD	Angle-Based Outlier Detection	2008	flowbench.unsupervised.pyod.ABOD
Probabilistic	KDE	Outlier Detection with Kernel Density Functions	2007	flowbench.unsupervised.pyod.KDE
Probabilistic	GMM	Probabilistic Mixture Modeling for Outlier Analysis		flowbench.unsupervised.pyod.GMM
Linear Model	PCA	Principal Component Analysis (the sum of weighted projected distances to the eigenvector hyperplanes)	2003	flowbench.unsupervised.pyod.PCA
Linear Model	OCSVM	One-Class Support Vector Machines	2001	flowbench.unsupervised.pyod.OCSVM
Linear Model	LMDD	Deviation-based Outlier Detection (LMDD)	1996	flowbench.unsupervised.pyod.LMDD
Proximity-Based	LOF	Local Outlier Factor	2000	flowbench.unsupervised.pyod.LOF
Proximity-Based	CBLOF	Clustering-Based Local Outlier Factor	2003	flowbench.unsupervised.pyod.CBLOF
Proximity-Based	kNN	k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score)	2000	flowbench.unsupervised.pyod.KNN
Outlier Ensembles	IForest	Isolation Forest	2008	flowbench.unsupervised.pyod.IForest
Outlier Ensembles	INNE	Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles	2018	flowbench.unsupervised.pyod.INNE
Outlier Ensembles	LSCP	LSCP: Locally Selective Combination of Parallel Outlier Ensembles	2019	flowbench.unsupervised.pyod.LSCP

• Example of using GMM

  from flowbench.pyod import GMM
  from flowbench.dataset import FlowDataset
  dataset = FlowDataset(root="./", name="1000genome")
  Xs = ds.x.numpy()
  clf = GMM()
  clf.fit(Xs)
  y_pred = clf.predict(Xs)
  

  • Detailed example in example/demo_pyod.py

PyGOD Models

Type	Abbr	Year	Class
Clustering	SCAN	2007	flowbench.unsupervised.pygod.SCAN
GNN+AE	GAE	2016	flowbench.unsupervised.pygod.GAE
MF	Radar	2017	flowbench.unsupervised.pygod.Radar
MF	ANOMALOUS	2018	flowbench.unsupervised.pygod.ANOMALOUS
MF	ONE	2019	flowbench.unsupervised.pygod.ONE
GNN+AE	DOMINANT	2019	flowbench.unsupervised.pygod.DOMINANT
MLP+AE	DONE	2020	flowbench.unsupervised.pygod.DONE
MLP+AE	AdONE	2020	flowbench.unsupervised.pygod.AdONE
GNN+AE	AnomalyDAE	2020	flowbench.unsupervised.pygod.AnomalyDAE
GAN	GAAN	2020	flowbench.unsupervised.pygod.GAAN
GNN+AE	DMGD	2020	flowbench.unsupervised.pygod.DMGD
GNN	OCGNN	2021	flowbench.unsupervised.pygod.OCGNN
GNN+AE+SSL	CoLA	2021	flowbench.unsupervised.pygod.CoLA
GNN+AE	GUIDE	2021	flowbench.unsupervised.pygod.GUIDE
GNN+AE+SSL	CONAD	2022	flowbench.unsupervised.pygod.CONAD
GNN+AE	GADNR	2024	flowbench.unsupervised.pygod.GADNR

• Example of using GMM

  from flowbench.unsupervised.pygod import GAE
  from flowbench.dataset import FlowDataset
  dataset = FlowDataset(root="./", name="1000genome")
  data = dataset[0]
  clf = GAE()
  clf.fit(data)
  

  • Detailed example in example/demo_pygod.py

Supervised Models

• Example of using MLP

  from flowbench.supervised.mlp import MLPClassifier
  from flowbench.dataset import FlowDataset
  dataset = FlowDataset(root="./", name="1000genome")
  data = dataset[0]
  clf = MLPClassifier()
  clf.fit(data)
  

  • Detailed example in example/demo_supervised.py

Supervised fine-tuned LLMs

• Example of using LoRA (Low-rank Adaptation) for supervised fine-tuned LLMs:

  from peft import LoraConfig
  dataset = load_dataset("cshjin/poseidon", "1000genome")
  # data processing
  ...
  # LoRA config
  peft_config = LoraConfig(task_type=TaskType.SEQ_CLS, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)
  training_args = TrainingArgument(...)
  # LoRA trainer
  trainer = Trainer(peft_model, ...)
  trainer.train()
  ...
  

  • Detailed example in example/demo_sft_lora.py